Shape Matching with Occlusion in Image Databases Aristeidis Diplaros Euripides G.M. Petrakis Evangelos Milios Technical University of Crete.

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Presentation transcript:

Shape Matching with Occlusion in Image Databases Aristeidis Diplaros Euripides G.M. Petrakis Evangelos Milios Technical University of Crete

= Given a shape database, retrieve shapes which are similar to an example shape. = Shape matching is the central problem of shape retrieval. = How similar? Problem description

Methodology = Main idea: Merging of a "noisy" sequence of segments and matching with one or more segments of the other shape. Merging of 3 segments of the upper curve And matching with one segment of the lower curve

= B-spline approximation. = Curvature: = Inflections points are given by k(u)=0. = Segments with k>0 are convex (C). = Segments with k<0 are concave (V). = The shape is transformed to a sequence of segments VCVC.

Shape A Dynamic Programming Table i Shape B j = transitions = matching of segments. = simple or compound transitions.

Shape Á i Shape Â j = No matching of C with V. = CVC...C -> C and VCV...V-> V. = Only half of the cells are filled with values.

= Matching cases

= The DP table consists of three distinct areas: = Initialization area (the first row) - filled with : = Termination area (the last row) – all complete paths end at cells in this area. = Computation area (the remaining rows)

= For each "accessible" cell we calculate the matching cost as: = Where: = Constant ë: = small ë favours merging. = large ë prevents merging.

= Three features are calculated for each segment: = l = arc length. = S = area between the chord and the arc. = ₩ = the angle traversed by the tangent to the segment.

Experimental results " Two datasets: " 1000 closed shaped " 1500 open shapes " 20 queries. " Precision / recall diagrams. " Human relevance judgments. " Demonstrate the superiority of our method over traditional shape matching method based on Fourier and Moments

Example

Closed dataset

Open dataset

Conclusions = Our approach handles occluded, noisy or deformed shapes and is independent of translation, scale, rotation, starting point selection and symmetric transformations of shapes. = Our evaluation indicates that our approach is well suited to shape matching and retrieval.